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In the Gaps Between Models

In my Anatomy of a Shot I hinted that we might measure different component parts of xG and compare them. That’s exactly what I’m going to do in this post – take what I call chance quality, a form of xG that includes positional data but excludes the shot itself, and compare it to my expected save value for that shot. Because think about what happens between those two measurements – the first model says, “in general, teams have such-and-such a chance of scoring from some sort of shot over here”, the second says “shit, did you see that? He must have a foot like a traction engine.”

What comes between those two models? Well, something resembling finishing quality, or at least good decision making. Even if a player isn’t converting a ton of chances, if they’re reliable making shots more difficult to save, they’re shooting well. If they’re taking prime quality chances but making them easy to save, well, maybe that’s rubbish shooting. That’s the theory at least, what do the numbers look like? Here’s everyone with 20+ shots in the Premier League this year:

Player

Shots

On Target

Goals

SoTR

Conv%

Chance Quality

Save Difficulty

SD/CQ

SD-CQ

Olivier Giroud

23

12

4

52.17%

17.39%

14.43%

20.57%

142.57%

6.14%

Juan Mata

20

7

3

35.00%

15.00%

9.86%

15.08%

153.01%

5.23%

Sergio Agüero

33

14

6

42.42%

18.18%

12.44%

17.64%

141.77%

5.20%

Bafétimbi Gomis

23

11

4

47.83%

17.39%

14.17%

17.06%

120.40%

2.89%

Harry Kane

33

12

2

36.36%

6.06%

9.43%

12.28%

130.20%

2.85%

Ross Barkley

28

9

2

32.14%

7.14%

5.30%

7.92%

149.30%

2.61%

Jamie Vardy

38

17

9

44.74%

23.68%

15.73%

17.96%

114.18%

2.23%

Sadio Mané

26

10

2

38.46%

7.69%

9.42%

11.65%

123.63%

2.23%

Yohan Cabaye

21

8

4

38.10%

19.05%

16.11%

17.99%

111.70%

1.88%

Romelu Lukaku

28

12

5

42.86%

17.86%

12.59%

13.46%

106.91%

0.87%

Riyad Mahrez

25

11

5

44.00%

20.00%

13.27%

13.70%

103.22%

0.43%

Theo Walcott

26

12

2

46.15%

7.69%

14.68%

15.09%

102.83%

0.42%

Odion Ighalo

26

9

5

34.62%

19.23%

9.56%

9.61%

100.49%

0.05%

Graziano Pellè

38

11

5

28.95%

13.16%

12.60%

12.33%

97.88%

-0.27%

Alexis Sánchez

45

15

6

33.33%

13.33%

12.17%

11.39%

93.53%

-0.79%

Memphis Depay

25

8

1

32.00%

4.00%

8.09%

7.13%

88.15%

-0.96%

Diafra Sakho

29

10

3

34.48%

10.34%

13.32%

11.82%

88.76%

-1.50%

Philippe Coutinho

39

11

1

28.21%

2.56%

7.23%

5.68%

78.63%

-1.54%

Santiago Cazorla

20

7

0

35.00%

0.00%

6.92%

5.28%

76.24%

-1.64%

Jonjo Shelvey

21

7

0

33.33%

0.00%

4.83%

2.92%

60.54%

-1.90%

Gnegneri Yaya Touré

26

8

1

30.77%

3.85%

9.23%

6.49%

70.28%

-2.74%

Rudy Gestede

22

7

3

31.82%

13.64%

10.96%

8.10%

73.97%

-2.85%

Aaron Ramsey

27

8

1

29.63%

3.70%

10.09%

6.94%

68.81%

-3.15%

Jason Puncheon

20

3

0

15.00%

0.00%

7.08%

1.74%

24.65%

-5.33%

Troy Deeney

23

4

0

17.39%

0.00%

8.43%

1.62%

19.28%

-6.80%

I should note that the save difficulty number here, because I want an aggregate over all their shots, counts off-target shots as a save difficulty on zero. The raw number obviously averages out roughly to the global conversion rate of on-target shots (around 30%). So, we can see some players increase the average difficulty of their shots for keepers, others make them easier. I’ve calculated both the ratio (i.e. Juan Mata increases his shots’ difficulty by 1.5x), and the difference, (i.e. Juan Mata increased his shot quality of around 10% to a save difficulty of around 15%).

To the right are better chances, top the top are better shots. You can see examples like Olivier Giroud and Sergio Agüero, who are making already quite good chances even scarier, Ross Barkley’s making bad chances look very slightly more exciting, and Jason Puncheon and Troy Deeney just need to stop.

Let’s look at a bigger sample, here’s 2014, 50+ shots:

Player

Shots

On Target

Goals

SoTR

Conv%

Chance Quality

Save Difficulty

SD/CQ

SD-CQ

Nacer Chadli

54

22

11

40.74%

20.37%

9.69%

16.00%

165.11%

6.31%

Steven Gerrard

55

22

10

40.00%

18.18%

13.18%

18.66%

141.62%

5.48%

Olivier Giroud

70

29

14

41.43%

20.00%

11.49%

15.67%

136.36%

4.18%

Diego Da Silva Costa

76

37

20

48.68%

26.32%

15.22%

19.36%

127.25%

4.15%

Harry Kane

113

48

22

42.48%

19.47%

11.65%

15.71%

134.87%

4.06%

David Silva

66

27

12

40.91%

18.18%

11.51%

15.26%

132.61%

3.75%

Eden Hazard

78

33

14

42.31%

17.95%

13.94%

16.87%

121.04%

2.93%

Aaron Ramsey

63

17

6

26.98%

9.52%

8.56%

10.81%

126.27%

2.25%

Wayne Rooney

79

27

12

34.18%

15.19%

10.89%

12.66%

116.25%

1.77%

Mame Biram Diouf

55

22

11

40.00%

20.00%

17.00%

18.71%

110.01%

1.70%

Robin van Persie

76

37

10

48.68%

13.16%

13.27%

14.92%

112.41%

1.65%

Ayoze Pérez Gutiérrez

61

24

7

39.34%

11.48%

10.37%

12.02%

115.85%

1.64%

Bafétimbi Gomis

69

24

7

34.78%

10.14%

9.50%

11.10%

116.76%

1.59%

Raheem Sterling

84

33

7

39.29%

8.33%

8.96%

10.52%

117.51%

1.57%

Jonjo Shelvey

63

20

4

31.75%

6.35%

7.44%

8.92%

119.87%

1.48%

Charlie Austin

130

53

18

40.77%

13.85%

12.41%

13.86%

111.67%

1.45%

Gylfi Sigurdsson

67

24

7

35.82%

10.45%

7.50%

8.91%

118.77%

1.41%

Kevin Mirallas

52

16

7

30.77%

13.46%

7.63%

8.76%

114.92%

1.14%

Sergio Agüero

148

62

26

41.89%

17.57%

14.82%

15.75%

106.32%

0.94%

Saido Berahino

86

37

14

43.02%

16.28%

13.67%

14.59%

106.71%

0.92%

Alexis Sánchez

121

49

16

40.50%

13.22%

9.97%

10.85%

108.90%

0.89%

Charlie Adam

62

17

7

27.42%

11.29%

7.53%

8.34%

110.69%

0.80%

Sadio Mané

60

25

10

41.67%

16.67%

11.93%

12.68%

106.30%

0.75%

Christian Eriksen

97

26

10

26.80%

10.31%

7.22%

7.94%

109.90%

0.71%

Christian Benteke

80

29

13

36.25%

16.25%

12.29%

12.96%

105.43%

0.67%

Leroy Fer

54

14

6

25.93%

11.11%

8.47%

9.02%

106.47%

0.55%

Stewart Downing

70

19

6

27.14%

8.57%

6.73%

7.14%

106.10%

0.41%

Riyad Mahrez

63

24

4

38.10%

6.35%

7.65%

7.97%

104.15%

0.32%

Gnegneri Yaya Touré

89

27

10

30.34%

11.24%

8.66%

8.98%

103.61%

0.31%

Romelu Lukaku

106

43

11

40.57%

10.38%

11.56%

11.65%

100.81%

0.09%

Nikica Jelavic

57

15

8

26.32%

14.04%

10.53%

10.55%

100.23%

0.02%

Diafra Sakho

66

22

10

33.33%

15.15%

13.53%

13.52%

99.97%

-0.00%

Craig Gardner

56

18

3

32.14%

5.36%

7.57%

7.49%

99.01%

-0.07%

Wilfried Bony

89

35

11

39.33%

12.36%

11.33%

11.15%

98.43%

-0.18%

Danny Ings

97

33

11

34.02%

11.34%

11.41%

10.65%

93.35%

-0.76%

Jordan Henderson

50

14

6

28.00%

12.00%

9.95%

9.17%

92.16%

-0.78%

Dusan Tadic

53

21

4

39.62%

7.55%

11.21%

10.32%

92.10%

-0.89%

Danny Welbeck

58

23

4

39.66%

6.90%

12.18%

11.24%

92.33%

-0.93%

Philippe Coutinho

103

34

5

33.01%

4.85%

6.13%

5.11%

83.40%

-1.02%

Willian Borges Da Silva

55

17

2

30.91%

3.64%

6.90%

5.70%

82.65%

-1.20%

Jason Puncheon

65

20

6

30.77%

9.23%

6.28%

5.06%

80.63%

-1.22%

Oscar dos Santos Emboaba Junior

72

23

6

31.94%

8.33%

8.43%

7.17%

85.05%

-1.26%

Santiago Cazorla

93

33

7

35.48%

7.53%

12.00%

10.44%

87.04%

-1.55%

Ángel Di María

61

18

3

29.51%

4.92%

6.06%

4.38%

72.26%

-1.68%

Yannick Bolasie

69

19

4

27.54%

5.80%

7.49%

5.80%

77.43%

-1.69%

Abel Hernández

52

19

4

36.54%

7.69%

11.66%

9.95%

85.33%

-1.71%

Gabriel Agbonlahor

53

17

6

32.08%

11.32%

11.33%

9.34%

82.39%

-2.00%

Ross Barkley

51

14

2

27.45%

3.92%

7.31%

5.27%

72.01%

-2.05%

Connor Wickham

83

24

5

28.92%

6.02%

9.04%

6.88%

76.11%

-2.16%

Enner Valencia

72

21

4

29.17%

5.56%

10.62%

8.05%

75.77%

-2.57%

Ashley Barnes

66

21

5

31.82%

7.58%

11.15%

8.33%

74.71%

-2.82%

Graziano Pellè

123

38

12

30.89%

9.76%

14.29%

10.80%

75.61%

-3.49%

Mario Balotelli

56

20

1

35.71%

1.79%

10.08%

6.51%

64.60%

-3.57%

Peter Crouch

59

17

8

28.81%

13.56%

13.07%

8.63%

65.99%

-4.45%

Which in turn looks like this:

Steven Gerrard’s numbers here are padded a bit by penalties, but he took good penalties, so you can see the boost he gets. Costa was a monster, Nacer Chadli was incredibly sharp (though seems to have crashed hard this season, basically halving the xG on every shot). Ross Barkley’s chances were just as bad, but unlike this year, they didn’t go in. Jason Puncheon just needs to stop.

So this is fun, but is it really any more interesting than conversion rate et al? Let’s look at how predictive each season is of the next. I’ll limit it to full seasons, players with 50+ shots. Here’s how various metrics perform:

Metric

R2

2011-2012

2012-2013

2013-2014

SoTR

0.1967

0.041

0.0215

Conversion

0.4299

0.1271

0.2224

Scoring %

0.1844

0.0228

0.0584

SD/SQ

0.2122

0.2436

0.1161

SD-SQ

0.3498

0.2729

0.0929

While it’s clear we haven’t found the holy gail of a strongly repeatable shooting metric, I still like our composite model. It has the benefit that as my chance quality and save difficulty models get better, these numbers may also improve, and I’ll be sure to look into that.

At the very least, I think the idea of having small, granular models, and looking at the gaps between them is an interesting way to find some new metrics and insights, and I’ll see what else I can find with a similar approach.